sis 18(19): e5

Research Article

Unsupervised Machine Learning based Documents Clustering in Urdu

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  • @ARTICLE{10.4108/eai.19-12-2018.156081,
        author={Atta Ur Rahman and Khairullah Khan and Wahab Khan and Aurangzeb Khan and Bibi Saqia},
        title={Unsupervised Machine Learning based Documents Clustering in Urdu},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        keywords={Urdu; Documents clustering; Similarity Measures; K-Means Algorithm},
  • Atta Ur Rahman
    Khairullah Khan
    Wahab Khan
    Aurangzeb Khan
    Bibi Saqia
    Year: 2018
    Unsupervised Machine Learning based Documents Clustering in Urdu
    DOI: 10.4108/eai.19-12-2018.156081
Atta Ur Rahman1,*, Khairullah Khan1, Wahab Khan2, Aurangzeb Khan1, Bibi Saqia1
  • 1: Department of Computer Science, University of Science & Technology Bannu, Pakistan
  • 2: Department of Computer Science & Software Engineering, IIU, Islamabad 44000, Pakistan
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The volume of data on the web is growing rapidly, due to the proliferation of news sources, contents, blogs and journals etc. Like other languages, the Urdu language has also observed tremendous growth on the internet. As the volume of data is expanding, information retrieval (IR) is becoming complicated. Document clustering is an unsupervised ML approach, employed to group a huge number of dispersed documents into a small number of significant and consistent clusters, thus providing a base for indexing, IR and browsing mechanisms. Documents clustering has a long tradition in English as well as English like western languages, but Urdu lags behind in terms sophisticated natural language processing (NLP) tools and resources for documents clustering. Documents clustering becomes a challenging task in Urdu language having a rich morphology, particular structure, syntax peculiarities and cursive nature. In this study, we have developed a framework of document clustering and analysed various similarity measures for Urdu documents. We have also checked the effect of stop words removal in the process of Urdu document clustering.